## No drafting

In a fascinating white paper, Bert Blocken, Professor of Civil Engineering at Eindhoven University of Technology, comments on social distancing when applied to walking, running or cycling. His point is that the government recommendations to maintain a distance of 1.5 or 2 metres assume people are standing still indoors or outdoors in calm weather. However, when a person is moving, the majority of particulate droplets are swept along in a trailing slipstream.

Cyclists typically prefer to ride closely behind each other, in order to benefit from the aerodynamic drafting effect. Cycling is currently a permitted form of exercise in the UK, though only if riding alone or with members of your household. Nevertheless, there may be times when you find yourself catching up with a cyclist ahead. In this situation, you should avoid the habitual tendency to move up into the slipstream of the rider in front.

Professor Blocken’s team has performed computational fluid dynamics (CFD) simulations showing the likely spread of micro-droplets behind people moving at different speeds. As the cloud of particles, produced when someone coughs or sneezes, is swept into the slipstream, the heavier droplets, shown in red in the diagram above, fall faster. These are generally thought to be more considerably more contagious. You can see that they can land on the hands and body of the following athlete.

Based on the results, Blocken advises to keep a distance of at least four to five meters behind the leading person while walking in the slipstream, ten meters when running or cycling slowly and at least twenty metres when cycling fast.

Social Distancing v2.0

The recommendation, for overtaking other cyclists, is to start moving into a staggered position some twenty metres behind the rider in front, consistently avoiding the slipstream as you pass.

The results will be reported in a forthcoming peer-reviewed publication. But given the importance of the topic, I recommend that you take a look at the highly accessible three page white paper available here.

## Modelling Strava Fitness and Freshness

Since my blog about Strava Fitness and Freshness has been very popular, I thought it would be interesting to demonstrate a simple model that can help you use these metrics to improve your cycling performance.

As a quick reminder, Strava’s Fitness measure is an exponentially weighted average of your daily Training Load, over the last six weeks or so. Assuming you are using a power meter, it is important to use a correctly calibrated estimate of your Functional Threshold Power (FTP) to obtain an accurate value for the Training Load of each ride. This ensures that a maximal-effort one hour ride gives a value of 100. The exponential weighting means that the benefit of a training ride decays over time, so a hard ride last week has less impact on today’s Fitness than a hard ride yesterday. In fact, if you do nothing, Fitness decays at a rate of about 2.5% per day.

Although Fitness is a time-weighted average, a simple rule of thumb is that your Fitness Score equates to your average daily Training Load over the last month or so. For example, a Fitness level of 50 is consistent with an average daily Training Load (including rest days) of 50. It may be easier to think of this in terms of a total Training Load of 350 per week, which might include a longer ride of 150, a medium ride of 100 and a couple of shorter rides with a Training Load of 50.

## How to get fitter

The way to get fitter is to increase your Training Load. This can be achieved by riding at a higher intensity, increasing the duration of rides or including extra rides. But this needs to be done in a structured way in order be effective. Periodisation is an approach that has been tried and tested over the years. A four-week cycle would typically include three weekly blocks of higher training load, followed by an easier week of recovery. Strava’s Fitness score provides a measure of your progress.

### Modelling Fitness and Fatigue

An exponentially weighted moving average is very easy to model, because it evolves like a Markov Process, having the following property, relating to yesterday’s value and today’s Training Load.
$F_{t} = \lambda * F_{t-1}+\left ( 1-\lambda \right )*TrainingLoad_{t}$
where
$F_{t}$ is Fitness or Fatigue on day t and
$\lambda = exp(-1/42) \approx 0.976$ for Fitness or
$\lambda = exp(-1/7) \approx 0.867$ for Fatigue

This is why your Fitness falls by about 2.4% and your Fatigue eases by about 13.3% after a rest day. The formula makes it straightforward to predict the impact of a training plan stretching out into the future. It is also possible to determine what Training Load is required to achieve a target level of Fitness improvement of a specific time period.

The change in Fitness over the next seven days is called a weekly “ramp”. Aiming for a weekly ramp of 5 would be very ambitious. It turns out that you would need to increase your daily Training Load by 33. That is a substantial extra Training Load of 231 over the next week, particularly because Training Load automatically takes account of a rider’s FTP.

Interestingly, this increase in Training Load is the same, regardless of your starting Fitness. However, stepping up an average Training Load from 30 to 63 per day would require a doubling of work done over the next week, whereas for someone starting at 60, moving up to 93 per day would require a 54% increase in effort for the week.

In both cases, a cyclist would typically require two additional hard training rides, resulting in an accumulation of fatigue, which is picked up by Strava’s Fatigue score. This is a much shorter term moving average of your recent Training Load, over the last week or so. If we assume that you start with a Fatigue score equal to your Fitness score, an increase of 33 in daily Training Load would cause your Fatigue to rise by 21 over the week. If you managed to sustain this over the week, your Form (Fitness minus Fatigue) would fall from zero to -16. Here’s a summary of all the numbers mentioned so far.

Whilst it might be possible to do this for a week, the regime would be very hard to sustain over a three-week block, particularly because you would be going into the second week with significant accumulated fatigue. Training sessions and race performance tend to be compromised when Form drops below -20. Furthermore, if you have increased your Fitness by 5 over a week, you will need to increase Training Load by another 231 for the following week to continue the same upward trajectory, then increase again for the third week. So we conclude that a weekly ramp of 5 is not sustainable over three weeks. Something of the order of 2 or 3 may be more reasonable.

### A steady increase in Fitness

Consider a rider with a Fitness level of 30, who would have a weekly Training Load of around 210 (7 times 30). This might be five weekly commutes and a longer ride on the weekend. A periodised monthly plan could include a ramp of 2, steadily increasing Training Load for three weeks followed by a recovery week of -1, as follows.

This gives a net increase in Fitness of 5 over the month. Fatigue has also risen by 5, but since the rider is fitter, Form ends the month at zero, ready to start the next block of training.

To simplify the calculations, I assumed the same Training Load every day in each week. This is unrealistic in practice, because all athletes need a rest day and training needs to mix up the duration and intensity of individual rides. The fine tuning of weekly rides is a subject for another blog.

### A tougher training block

A rider engaging in a higher level of training, with a Fitness score of 60, may be able to manage weekly ramps of 3, before the recovery week. The following Training Plan would raise Fitness to 67, with sufficient recovery to bring Form back to positive at the end of the month.

### A general plan

The interesting thing about this analysis is that the outcomes of the plans are independent of a rider’s starting Fitness. This is a consequence of the Markov property. So if we describe the ambitious plan as [3,3,3,-2], a rider will see a Fitness improvement of 7, from whatever initial value prevailed: starting at 30, Fitness would go to 37, while the rider starting at 60 would rise to 67.

Similarly, if Form begins at zero, i.e. the starting values of Fitness and Fatigue are equal, then the [3,3,3,-2] plan will always result in a in a net change of 6 in Fatigue over the four weeks.

In the same way, (assuming initial Form of zero) the moderate plan of [2,2,2,-1] would give any rider a net increase of Fitness and Fatigue of 5.

## Cycling Physique

It is easy to assume that successful professional cyclists are all skinny little guys, but if you look at the data, it turns out that they have an average height of 1.80m and an average weight of around 68kg. If we are to believe the figures posted on ProCyclingStats, hardly any professional cyclists would be considered underweight. In fact, they would struggle to perform at the required level if they did not maintain a healthy weight.

### Taller than you might think

According to a study published in 2013 and updated in 2019, the global average height of adult males born in 1996 was 1.71m, but there is considerable regional variation. The vast majority of professional cyclists come from Europe, North America, Russia and the Antipodes where men tend to be taller than those from Asia, Africa and South America. For the 41 Colombians averaging 1.73m, there are 85 Dutch riders with a mean height of 1.84m. See chart below.

Furthermore, road cycling involves a range of disciplines, including sprinting and time trialling, where size and raw power provide an advantage. The peloton includes larger sprinters alongside smaller climbers.

### Not as light as expected

While 68kg for a 1.80m male is certainly slim, it equates to a body mass index of 21 (BMI = weight / (height)²), which is towards the middle of the recommended healthy range. BMI is not a sophisticated measure, as it does not distinguish between fat and muscle. Since muscle is more dense than fat and cyclists tend to have it a higher percentage of lean body mass, they will look slimmer than a lay person of equivalent height and weight. Nevertheless doctors use BMI as a guide and become concerned when it falls below 18.5.

The chart includes over 1,100 professional cyclists, but very few pros would be considered underweight. The majority of riders have a BMI of between 20 and 22. Although Colombian riders (red) tend to be smaller, specialising in climbing, their average BMI of 20.8 is not that different from larger Dutch riders (orange) with a mean BMI of 21.2. The taller Colombians include the sprinters Hodeg, Gaviria and Molano.

### Types of rider

This chart shows the names of a sample of top riders. All-out sprinters tend to have a BMI of around 24, even if they are small like Caleb Ewan. Sprints at the end of more rolling courses are likely to be won by riders with a BMI of 22, such as Greipel, van Avermaet, Sagan, Gaviria, Groenewegen, Bennet and Kwiatkowski. Time trial specialists like Dennis and Thomas have similar physiques, though Dumoulin and Froome are significantly lighter and remarkably similar to each other.

GC contenders Roglic, Kruiswijk and Gorka Izagirre are near the centre of the distribution with a BMI around 21, close to Viviani, who is unusually light for a sprinter. Pinot, Valverde, Dan Martin, the Yates brothers and Pozzovivo appear to be light for their heights. Interestingly climbers such as Quintana, Uran, Alaphilippe, Carapaz and Richie Porte all have a BMI of around 21, whereas Lopez is a bit heavier.

If the figures reported on ProCyclingStats are accurate, George Bennet and Emanuel Buchmann are significantly underweight. Weighting 58kg for a height of 1.80m does not seem to be conducive to strong performance, unless they are extraordinary physical specimens.

### Conclusions

Professional cyclists are lean, but they would not be able to achieve the performance required if they were underweight. It is possible that the weights of individual riders might vary over time by a couple of kilos, moving them a small amount vertically on the chart, but scientific approaches are increasingly employed by expert nutritionists to avoid significant weight loss over longer stage races. The Jumbo Foodcoach app was developed alongside the Jumbo-Visma team and, working with Team Sky, James Morton strove to ensure that athletes fuel for the work required. Excessive weight loss can lead to a range of problems for health and performance.

## Relative Energy Deficit in Sport (RED-S)

Unfortunately an increasing proportion of the population of western society has fallen into the habit consuming far more calories than required, resulting an a huge increase in obesity, with all the associated negative health consequences. At the opposite end of the spectrum, a smaller but important group experiences problems stemming from insufficient energy intake. This group includes certain competitive athletes, especially those involved in sports or dance, where a low body weight confers a performance advantage. A new infographic draws attention to this problem and highlights the fact that the individuals have control over the factors that can put them on the path to optimal health and performance.

## RED-S

The human body requires a certain amount of energy to perform normal metabolic functions, including, maintaining homeostasis, cardiac and brain activity. The daily requirement is around 2,000 kcal for women and 2,500 kcal for men. Additional energy intake is required to balance the energy requirements any physical activities performed.

Athletes and dancers need to eat more than sedentary people, but they can fall into an energy deficit in two ways.

• Reducing energy intake, while maintaining the same training load. This is typically an intentional decision, in order to lose weight, in the belief that this might improve performance. It can also arise unintentionally, perhaps due to failing to calculate energy demands of the training programme.
• Increasing training load, while maintaining the same energy intake. This can often occur unintentionally, as a result of a more intensive training session or a shift into a higher training phase. Some athletes or dancers perform extra training sessions while deliberately failing to eat more, in the hope, once again, that this might improve performance.

While most of the population would benefit from a period of moderate energy deficit. High level athletes and dancers tend to be very lean, to the extent that losing further weight compromises health and performance. The reason is that the endocrine system is forced to react to an energy deficit by scaling back or shutting down key metabolic systems. For example, levels of the sex hormones testosterone and oestrogen can fall, leading to, among other things, reductions in bone density. Unlike men, women have a warning sign, in the form of an interruption or cessation of menstruation. Both men and women with RED-S are likely to suffer from a failure to achieve their peak athletic performance.

## Achieving peak performance

Fortunately athletes have control over the levers that lead to peak performance. These are nutrition, training load and, of course, recovery. Consistently fuelling for the energy required, whilst ensuring that the body has adequate time to recover, allows the endocrine system to trigger the genes that lead to the beneficial outcomes of exercise, such as improved cardiovascular efficiency, effective muscular development, optimal body composition, healthy bones and a fully functional immune system. These are the changes required to reach the highest levels of performance.

## Don’t ride your bike like an astronaut

Astronauts return from the International Space Station with weak bones, due to the lack of gravitational forces. It is surprising to learn that competitive cyclists can experience similar losses in bone density over the period of a race season.

The problem is called Relative Energy Deficiency is Sport (RED-S). This occurs when lean athletes reach a tipping point where the benefits of losing weight become overwhelmed by negative impacts on health. When deprived of sufficient energy intake to match training load, certain metabolic systems become impaired or shut down.

Colleagues from Durham University and I recently published a study investigating what cyclists at risk of RED-S can do to improve their health and performance. It is freely available and written in an accessible way, without the requirement for specialist expertise.

## Race performance

Race performance was measured by the number of British Cycling points accumulated over the season. This was correlated with power (FTP and FTP/kg) and training load. However, changes in energy availability proved to be an important factor. After adjusting for FTP, cyclists who improved their fuelling (green triangles) gained, on average, 95 points more than those who made no change. In contrast, those who restricted their nutrition (red crosses) accumulated 95 fewer points and reported fatigue, illness and injury.

The nutritional advice included recommendations on adequate fuelling before, during and after rides. Also see my previous article on fuelling for the work required.

## Bone health

Competitive road cyclists can fall into an energy deficit due to the long hours of training they complete. Although an initial loss of excess body weight can lead to performance improvements, athletes need to maintain a healthy body mass. The lumbar spine is particularly sensitive to deficiencies of energy availability.

In cyclists, the lower back also fails to benefit from the gravitational stresses of weight-bearing sports. This is why, in addition to nutritional advice, study participants were recommended some basic skeletal loading exercises (yes, that is me in the pictures).

The cyclists fell into three general groups: those who made positive changes to nutrition and skeletal loading, those who made negative changes and the remainder. The resulting changes in bone mineral density over a six month period were striking, with highly statistically significant differences observed between the groups.

Those making positive changes (green triangles) saw significant gains in bone mineral density, while those making negative changes (red crosses) saw equally significant negative losses in bone density. Any individual observation outside the band of the least significant change (LSC) is indicative of a material change in bone health.

## Conclusions

The study provided strong evidence of the benefits of positive changes and the costs of negative changes in nutrition and skeletal loading exercises. It was noted that certain cyclists found it hard to overcome psychological barriers preventing them from deviating from their current routines. It is hoped that such strong statistical results will help these vulnerable athletes make beneficial behavioural changes

## References

Clinical evaluation of education relating to nutrition and skeletal loading in competitive male road cyclists at risk of relative energy deficiency in sports (RED-S): 6-month randomised controlled trial, Nicola Keay, Gavin Francis, Ian Entwistle, Karen Hind. BMJ Open Sport and Exercise Medicine Journal, Volume 5, Issue 1. http://dx.doi.org/10.1136/bmjsem-2019-000523

## Fuel for the work required: periodisation of carbohydrate intake

Last week I attended an event announcing the forthcoming launch of a new fitness app called Pillar. It offers combined training and nutrition advice to help athletes achieve their goals. Pillar is backed by a strong scientific team including Professor James Morton, Team Sky Head of Performance Nutrition, and Professor Graeme Close, England Rugby Head of Performance Nutrition.

James Morton gave a fascinating presentation about the periodisation of carbohydrate (CHO) fuelling, including a detailed description of the nutrition strategy he created to support Chris Froome’s famous 80km attack on stage 19 of the 2018 Giro d’Italia. His recent paper explains the underlying science. These are some of the key points.

• Always go into competition fully fuelled with carbohydrate
• Well-fuelled athletes perform for longer at higher intensities than those with depleted reserves
• Basic biochemistry: fat burning is too slow and supplies of the phosphocreatine are too small to sustain intensities over 85% of VO2max
• Theory is backed up by experiment
• There are pros and cons to training with low levels of carbohydrate
• Positive effects: Improved fat burning, changes in cell signalling, gene expression and enzyme/protein activity, potential to save precious glycogen stores for crucial attacks later in a race
• Negative effects: Inconsistent evidence of improved performance, ability to complete training session may be compromised, reduced immunity, risks to bone health, loss of top end for those on high fat/low carb (ketogenic) diet
• Different ways to train with low carbohydrate
• doing two sessions in one day with minimal refuelling
• low carb evening meal and breakfast: sleep low, train low the next morning
• fasted rides
• high fat/low carb diet

Is there a structured method of training that provides the benefits without the negatives?

• The authors propose a glycogen threshold hypothesis
• Positive effects seem to be dependent on commencing with muscle glycogen levels within a specific range
• Levels have to be low enough to promote positive effects
• But when too low, protein synthesis may be impaired and the ability to complete sessions is compromised
• This leads to the idea of periodising carbohydrate consumption, meal by meal, around planned training sessions
• “Fuelling for the work required”
• low carbs before and during lighter training sessions
• high carbs in preparation for and during rides with greater intensities
• always refuel after training
• The diagram above provides an example for an elite endurance cyclist
• The red, amber, green colour coding indicates low, medium or high carbohydrate consumption
• On day 1, the athlete aims to “train high” for a hard session
• A lighter evening meal on day 1 prepares to “sleep low, train low” ahead of a lower intensity session on day 2
• Carbohydrate intake rises after exercise on day 2 in anticipation of a high intensity session on day 3
• Fuelling is moderated on the evening of day 3 as day 4 is assigned as a recovery day
• Carbohydrate rises later on day 4 to prepare for the next block of training
• The Pillar app aims to provide these leading edge scientific principles to amateur cyclists and other athletes

In order to put this into action, you need to know how much carbohydrate you are consuming. My assumption has been that my diet is reasonably healthy, but I have never actually measured it. So I have been experimenting with free app MyFitnessPal that can be downloaded onto your phone. This provides a simple and convenient way to track the nutritional composition of your diet, including a barcode scanner that recognises most foods. You can link it to other apps such as Training Peaks to take account of energy expended. However, neither of these tools plans nutrition ahead of training sessions. Pillar aims to fill this gap. It will be interesting to see whether this turns out to be successful.

## References

Fuel for the Work Required: A Theoretical Framework for Carbohydrate Periodization and the Glycogen Threshold Hypothesis, SG Impey, MA Hearris, KM Hammond, JD Bartlett, J Louis, G Close, JP Morton, Sports Med (2018) 48:1031–1048, https://doi.org/10.1007/s40279-018-0867-7

Fuel for the work required: a practical approach to amalgamating train-low paradigms for endurance athletes, Impey SG, Hammond KM, Shepherd SO, Sharples AP, Stewart C, Limb M, Smith K, Philp A, Jeromson S, Hamilton DL, Close GL, Morton JP, Physiol Rep. 2016 May;4(10). pii: e12803. doi: 10.14814/phy2.12803

Low carbohydrate, high fat diet impairs exercise economy and negates the performance benefit from intensified training in elite race walkers, Burke LM, Ross ML, Garvican-Lewis LA, Welvaert M, Heikura IA, Forbes SG, Mirtschin JG, Cato LE, Strobel N, Sharma AP, Hawley JA.  J Physiol. 2017;595:2785–807

Low energy availability assessed by a sport-specific questionnaire and clinical interview indicative of bone health, endocrine profile and cycling performance in competitive male cyclists, BMJ Open Sport & Exercise Medicine,https://doi.org/10.1136/bmjsem-2018-000424